In this work, an artificial neural network based on the Cuckoo search algorithm (CS-ANN) is implemented for squeezing flow problems. Three problems are considered: the squeezing flow, the MHD squeezing flow, and the flow of the third-grade fluid past a moving belt. First, the approximation for the said nonlinear differential equations is explained and the proposed problems are transformed into the L2 norms of minimization problems. Then, a well-known Cuckoo search algorithm is used to minimize the norms of each problem to get the best set of weights for artificial neural networks. The outcome of the proposed method is displayed through graphs. Two cases for each problem are discussed consisting of the solution, error, weights, and fitness function, respectively. The numerical results for the state variables are displayed in Tables. The error analysis in each case proves the accuracy of our implemented technique. The results are validated through graphs by comparing CS-ANN results with the gradient descent method.
Nature‐inspired optimization techniques are useful tools in electrical engineering problems to minimize or maximize an objective function. In this paper, we use the firefly algorithm to improve the optimal solution for the problem of directional overcurrent relays (DOCRs). It is a complex and highly nonlinear constrained optimization problem. In this problem, we have two types of design variables, which are variables for plug settings (PSs) and the time dial settings (TDSs) for each relay in the circuit. The objective function is to minimize the total operating time of all the basic relays to avoid unnecessary delays. We have considered four models in this paper which are IEEE (3‐bus, 4‐bus, 6‐bus, and 8‐bus) models. From the numerical results, it is obvious that the firefly algorithm with certain parameter settings performs better than the other state‐of‐the‐art algorithms.
This article aims to analyze the two-dimensional (2D) nanofluid (Ag/C 2 H 6 O 2 ) flow past an exponentially stretched sheet. The magnetic field impact, heat source/sink, and convection in the thermal profile are taken into account. The complexity of the problem is reduced by introducing a dimensionless group of functions. The reduced model is transformed into a system of first-order ordinary differential equations (ODEs). This system is further analyzed with the artificial neural network (ANN), which is trained using the Levenberg–Marquardt algorithm. The whole dataset is sub divided into three parts: training ( 70% ), validation ( 15% ), and testing ( 15% ). The impact of nonlinear heat source/sink parameter, magnetic parameter, volume fraction of nanoparticles, and Prandtl number is displayed through graphs. The heat source, volume fraction, and the Prandtl number cause an increase in the thermal profile with its larger values. The magnetic parameter causes a decline in both the thermal and momentum boundary layers with its higher values. The analysis shows that the thermal energy profile is enhanced with the larger values of the volume fraction of silver nanoparticles and heat source. For each case study, the residual error (RE), regression line, and validation of the results are presented. The performance of the proposed methodology is numerically tabulated for the nanoparticle volume fraction shown in Table 3 , where the minimum absolute error (AE) is 5.3373e−11 at ϕ=0.05 . Based on this, we recommend ϕ=0.05 for better performance. The AEs for the ANN and bvp4c are computed for the state variables in Tables for the magnetic parameter M=5,10 , and 15. These tables show the overall performance of the ANN and further validate the present study. We have also validated the results of the ANN through the mean squared error graphically, where the accuracy of the proposed methodology is proven.
As part of the Sino-Pak trans-boundary cooperation for conservation and sustainable development in Pamir border region,World Wild Fund (WWF)-Pakistan conducted a preliminary social,economic and ecological survey in the Shimshal-Pamir Lakes area in July 2009.The purpose of the study was to explore potentials and opportunities for future collaborative conservation of some species,habitats and high altitude ecosystems in the border region between China and Pakistan.The two-week herpetological study in the Shimshal Pamir area of Khunjerab National Park (KNP) along Pakistan-China border was an integral part of the survey,conducted exclu-sively to document reptilian fauna with a special emphasis on investigating their occurrence,distribution and status in the study area.Field investigations were performed during daytime when it was hot enough and reptiles were active,basking or feeding.A total of 15 specimens belonging to four species of the Agamidae family were captured by striking stones and beating bushes with sticks.Collected specimens were preserved using 10% formalin solution,tagged with field information and stored in Zoological Survey Department,Karachi for future reference.Laboratory investigations were carried out for pholidosic counts and morphometric measurements.A detailed review of relevant literature,habitat characteristics and laboratory investigations revealed the occurrence of Laudakia himalayana,L.pakistanica,L.tuberculata and L.badakhshana at 4,082 m,4,172 m,4,005 m and 4,240 m asl,respectively,which are much higher altitudes as compared to the previously reported heights of 3,353 m,3,200 m,2,500 m and 2,400 m asl.The terrain offers a variety of ecological barriers,in the form of fast and freezing running waters and massive glaciers with peculiar harsh climatic conditions prevailing for nine months of the year,which restricts species migration and thus increases endemism.Although one of the four species recorded from the study area,i.e.L.pakistanica is endemic to Pakistan,L.tuberculata and L.badakhs
In the last four decades, China has transited from a closed country to major power status by adopting pragmatic policies. This article analyzes the transformation in Chinas foreign policy with its futuristic plan of building a harmonious world through peaceful co-existence and win-win cooperation. By adhering to principles of Panchsheel, foundations for prosperous China were laid. The notion of peaceful coexistence is the hallmark of Chinese foreign policy which has helped in resolving border issues withs neighbors by peaceful means. By remaining neutral and playing the role of a mediator, China has successfully managed regional and global conflicts. President Xi Jinpings vision of the Chinese Dream is, in fact, a holistic concept for taking China forward among leading nations in the world and rejuvenation of the Chinese nation for a prosperous future. Chinese foreign and domestic policies are now delivering and Pakistan being its Iron Brother can gain opportunities from Chinas peaceful rise
In this article, we considered a 3D symmetric flow of a ternary hybrid nanofluid flow (THNF) past a nonlinear stretching surface. The effect of the thermal radiation is considered. The THNF nanofluid SiO2+Cu+MoS2/H2O is considered in this work, where the shapes of the particles are assumed as blade, flatlet, and cylindrical. The problem is formulated into a mathematical model. The modeled equations are then reduced into a simpler form with the help of suitable transformations. The modeled problem is then tackled with a new machine learning approach known as a hybrid cuckoo search-based artificial neural network (HCS-ANN). The results are presented in the form of figures and tables for various parameters. The impact of the volume fraction coefficients ϕ1, ϕ2, and ϕ3, and the radiation parameter is displayed through graphs and tables. The higher numbers of the radiation parameter (Rd) and the cylinder-shaped nanoparticles, ϕ3, enhance the thermal profile. In each case, the residual error, error histogram, and fitness function for the optimization problem are presented. The results of the HCS-ANN are validated through mean square error and statistical graphs in the last section, where the accuracy of our implemented technique is proved.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
'/%3e%3cpath fill='%23fff' d='m8.751-17.959 10.11 11.373L3.997-9.844l13.94-6.1-7.692 13.129z'/%3e%3c/svg%3e)
